GGAnimate

Package Dependencies/ Data

Load Packages

First I need to load up the packages I’ll need

library(sf)

## Linking to GEOS 3.4.2, GDAL 2.1.2, proj.4 4.9.1

library(ggplot2) #development version!
## devtools::install_github("tidyverse/ggplot2")
library(tidyverse)

## Loading tidyverse: tibble
## Loading tidyverse: tidyr
## Loading tidyverse: readr
## Loading tidyverse: purrr
## Loading tidyverse: dplyr

## Conflicts with tidy packages ----------------------------------------------

## filter(): dplyr, stats
## lag():    dplyr, stats

library(readr)
library(cowplot)

## 
## Attaching package: 'cowplot'

## The following object is masked from 'package:ggplot2':
## 
##     ggsave

library(sp)
library(gridExtra)

## 
## Attaching package: 'gridExtra'

## The following object is masked from 'package:dplyr':
## 
##     combine

library(dplyr)
library(ggrepel)
library(plyr)

## -------------------------------------------------------------------------

## You have loaded plyr after dplyr - this is likely to cause problems.
## If you need functions from both plyr and dplyr, please load plyr first, then dplyr:
## library(plyr); library(dplyr)

## -------------------------------------------------------------------------

## 
## Attaching package: 'plyr'

## The following objects are masked from 'package:dplyr':
## 
##     arrange, count, desc, failwith, id, mutate, rename, summarise,
##     summarize

## The following object is masked from 'package:purrr':
## 
##     compact

library(gapminder)
#devtools::install_github("dgrtwo/gganimate", force=TRUE)
library(gganimate)

Import Postcode Data

Now I import my data. I filter for the Arran postcodes, (since Arran all begins ‘KA27’).

#Add download commands for data.
## Finding the Arran coordinates
arrancoordinates <- read.csv("../alldata/ukpostcodes.csv") %>%
 filter(substr(postcode,1,4)=="KA27")

#Find way to replace with existing SIMD shape files
arransubsect <- read_sf("../alldata/Scotland_pcs_2011") %>%
filter(substr(label,1,4)=="KA27")

Import SIMD data

Now I load the SIMD data, containing the geometries (shapefiles) and SIMD data (percentiles, etc)

reorderedvector<- c("S01011174", "S01011171", "S01011177", "S01011176", "S01011175", "S01011173", "S01011172" )

arran2016 <- read_sf("../alldata/SG_SIMD_2016")[c(4672,4666,4669,4671,4667,4668,4670),] %>%
  slice(match(reorderedvector, DataZone))

Arrandz2012 <- c(4409,4372,4353,4352,4351,4350,4349)

arran2012 <- read_sf("../alldata/SG_SIMD_2012")[Arrandz2012,]
arran2009 <- read_sf("../alldata/SG_SIMD_2009")[Arrandz2012,]
arran2006 <- read_sf("../alldata/SG_SIMD_2006")[Arrandz2012,]
arran2004 <- read_sf("../alldata/SG_SIMD_2004")[Arrandz2012,]

sharedvariables <- intersect(colnames(arran2016), colnames(arran2012)) %>%
  intersect(colnames(arran2009))  %>%
  intersect(colnames(arran2006))  %>%
  intersect(colnames(arran2004))
  
arran20162 <- arran2016 %>%
  select(sharedvariables) %>%
  mutate(year="2016")
arran20122 <- arran2012 %>%
  select(sharedvariables) %>%
  mutate(year="2012")
arran20092 <- arran2009 %>%
  select(sharedvariables) %>%
  mutate(year="2009")
arran20062 <- arran2006 %>%
  select(sharedvariables) %>%
  mutate(year="2006")
arran20042 <- arran2004 %>%
  select(sharedvariables) %>%
  mutate(year="2004")

arransimd <- rbind(arran20162,arran20122,arran20092,arran20062,arran20042) %>%
mutate(
    lon = map_dbl(geometry, ~st_centroid(.x)[[1]]),
    lat = map_dbl(geometry, ~st_centroid(.x)[[2]])
    )

arransimd$listID <- revalue(arransimd$DataZone,
               c("S01004409"="S01004409/S01011174", "S01004372"="S01004372/S01011171", "S01004353"="S01004353/S01011177", "S01004352"="S01004352/S01011176", "S01004351"="S01004351/S01011175", "S01004350"="S01004350/S01011173", "S01004349"="S01004349/S01011172", "S01011174"="S01004409/S01011174", "S01011171"="S01004372/S01011171", "S01011177"="S01004353/S01011177", "S01011176"="S01004352/S01011176", "S01011175"="S01004351/S01011175", "S01011173"="S01004350/S01011173", "S01011172"="S01004349/S01011172"))

Now I want to overlay the postcodes by Datazone. To do this I’ve converted both the Arran coordinates and Arran (2016) shapefiles into spatial points/polygons, converted them into a common CRS, and then compared them by using ‘plyr::over()’. This gives me the object ‘namingdzpostcode’, with the postcode rows grouped into IDs (unidentified datazones).

simple.sf <- st_as_sf(arrancoordinates, coords=c('longitude','latitude'))
st_crs(simple.sf) <- 4326

exampleshapes <- sf:::as_Spatial(arran2016$geometry) %>%
  spTransform(CRS("+proj=longlat +datum=WGS84"))

examplepoints <- sf:::as_Spatial(simple.sf$geom) %>%
  spTransform(CRS("+proj=longlat +datum=WGS84"))

namingdzpostcode <- over(exampleshapes, examplepoints, returnList = TRUE)

I can then take a member reference from the orginal postcode list, which gives me a selection of the rows in that DZ. For simplicity I’ve written this as a new function. ##Mutate arrancoordinates to label the IDs

function100 <- function(argument) 
{
  argument <- arrancoordinates[namingdzpostcode[[argument]],] %>% mutate(DataZone=argument)
}

arrancoordinates <- lapply(1:7,function100)
arrancoordinates <- rbind(arrancoordinates[[1]], arrancoordinates[[2]], arrancoordinates[[3]], arrancoordinates[[4]], arrancoordinates[[5]], arrancoordinates[[6]], arrancoordinates[[7]])

arrancoordinates$listID <- revalue(as.character(arrancoordinates$DataZone),
               c('1'="S01004409/S01011174", '2'="S01004372/S01011171", '3'="S01004353/S01011177", '4'="S01004352/S01011176", '5'="S01004351/S01011175", '6'="S01004350/S01011173", '7'="S01004349/S01011172"))

Labelling the namingdzpostcode list

names(namingdzpostcode) <- c(unique(arransimd$listID))

//

Mapping

library(rgdal)

## rgdal: version: 1.2-7, (SVN revision 660)
##  Geospatial Data Abstraction Library extensions to R successfully loaded
##  Loaded GDAL runtime: GDAL 2.1.2, released 2016/10/24
##  Path to GDAL shared files: /Library/Frameworks/R.framework/Versions/3.4/Resources/library/rgdal/gdal
##  Loaded PROJ.4 runtime: Rel. 4.9.1, 04 March 2015, [PJ_VERSION: 491]
##  Path to PROJ.4 shared files: /Library/Frameworks/R.framework/Versions/3.4/Resources/library/rgdal/proj
##  Linking to sp version: 1.2-4

library(leaflet)
library(ggmap)

## 
## Attaching package: 'ggmap'

## The following object is masked from 'package:cowplot':
## 
##     theme_nothing

Coordinates

postcodelist <- paste(unique(arrancoordinates$listID), "Postcodes", sep=" ")
datazonelist <- paste(unique(arrancoordinates$listID), "Datazones", sep=" ")

m = leaflet() %>% addTiles() %>% setView(-5.227680, 55.582338, zoom = 10) 

Example Markers

Inputing example markers.

cliniccoordinates <- read.csv("../alldata/clinics.csv") %>%
dplyr::left_join(arrancoordinates, by="postcode")

## Warning: Column `postcode` joining factors with different levels, coercing
## to character vector

#change to character
cliniccoordinates$X <- as.character(cliniccoordinates$X)

exampleshapes2 <- as(arransimd, "Spatial") %>%
spTransform(CRS("+proj=longlat +datum=WGS84"))

Creating the percentile labels

listlistlist <- paste(datazonelist, exampleshapes2$Percentile, sep=" ") %>%
paste("%", sep="")

Creating animated maps

p <- arransimd %>%
  ggplot() +
  geom_sf(aes(fill = Percentile, frame = year)) +
  theme_grey() +
  geom_text(aes(label = Percentile, x = lon, y = lat, frame = year), size = 5, colour = "white") +
  theme(axis.text.x=element_blank(),
        axis.ticks.x=element_blank(),
        axis.text.y=element_blank(),
        axis.ticks.y=element_blank(),
        axis.title.x = element_blank(),
        axis.title.y = element_blank()) +
  theme(legend.position="bottom")  

## Warning: Ignoring unknown aesthetics: frame

## Warning: Ignoring unknown aesthetics: frame

gganimate(p)

gganimate(p, "output2.gif")

## Executing: 
## convert -loop 0 -delay 100 Rplot1.png Rplot2.png Rplot3.png
##     Rplot4.png Rplot5.png 'output2.gif'

## Output at: output2.gif

Creating animated maps over ggmap

map <- get_map(location = c(lon = -5.227680, lat =55.582338)) %>%
  ggmap() +
  geom_sf(data = arransimd, aes(fill = Percentile, frame = year)) +
  coord_sf(crs= 4326, datum = sf::st_crs(4326)) +
  theme_grey() +
  theme(axis.text.x=element_blank(),
        axis.ticks.x=element_blank(),
        axis.text.y=element_blank(),
        axis.ticks.y=element_blank(),
        axis.title.x = element_blank(),
        axis.title.y = element_blank()) +
  theme(legend.position="bottom")

## Map from URL : http://maps.googleapis.com/maps/api/staticmap?center=55.582338,-5.22768&zoom=10&size=640x640&scale=2&maptype=terrain&language=en-EN&sensor=false

## Warning: Ignoring unknown aesthetics: frame

gganimate(map)

gganimate(map, "output3.gif")

## Executing: 
## convert -loop 0 -delay 100 Rplot1.png Rplot2.png Rplot3.png
##     Rplot4.png Rplot5.png 'output3.gif'

## Output at: output3.gif

// Timeline

library(htmlwidgets)
library(htmltools)
library(leaflet)
library(geojsonio)

#Build data.frame with 10 obs + 3 cols
power <- data.frame(
  "Latitude" = c(33.515556, 38.060556, 47.903056, 49.71, 49.041667, 31.934167, 54.140586, 54.140586, 48.494444, 48.494444),
  "Longitude" = c(129.837222, -77.789444, 7.563056, 8.415278, 9.175, -82.343889, 13.664422, 13.664422, 17.681944, 17.681944),
  "start" = do.call(
    "as.Date",
    list(
      x = c("15-Sep-1971", "1-Dec-1971", "1-Feb-1972", "1-Feb-1972", "1-Feb-1972", "1-Feb-1972", "1-Apr-1972", "1-Apr-1972", "24-Apr-1972", "24-Apr-1972"),
      format = "%d-%b-%Y"
    )
  )
)

# set start same as end
#  adjust however you would like
power$end <- power$start


# use geojsonio to convert our data.frame
#  to GeoJSON which timeline expects
power_geo <- geojson_json(power,lat="Latitude",lon="Longitude")

# create a leaflet map on which we will build
leaf <- leaflet() %>%
  addTiles()

# add leaflet-timeline as a dependency
#  to get the js and css
leaf$dependencies[[length(leaf$dependencies)+1]] <- htmlDependency(
  name = "leaflet-timeline",
  version = "1.0.0",
  src = c("href" = "http://skeate.github.io/Leaflet.timeline/"),
  script = "javascripts/leaflet.timeline.js",
  stylesheet = "stylesheets/leaflet.timeline.css"
)

# use the new onRender in htmlwidgets to run
#  this code once our leaflet map is rendered
#  I did not spend time perfecting the leaflet-timeline
#  options
leaf %>%
  setView(44.0665,23.74667,2) %>%
  onRender(sprintf(
    '
function(el,x){
    var power_data = %s;

    var timeline = L.timeline(power_data, {
      pointToLayer: function(data, latlng){
        var hue_min = 120;
        var hue_max = 0;
        var hue = hue_min;
        return L.circleMarker(latlng, {
          radius: 10,
          color: "hsl("+hue+", 100%%, 50%%)",
          fillColor: "hsl("+hue+", 100%%, 50%%)"
        });
      },
      steps: 1000,
      duration: 10000,
      showTicks: true
    });
    timeline.addTo(HTMLWidgets.find(".leaflet"));
}
    ',
    power_geo
))

https://stackoverflow.com/questions/36554605/cant-loop-with-rs-leaflet-package-to-produce-multiple-maps/36587525